# 使用k-近邻算法
#  import numpy as np
from numpy import *
import os
import operator

# 创建一个数据集
def createDataSet():
    group = array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])
    labels = ['A', 'A', 'B', 'B']
    return group, labels 

###########
#### 也就是说group就是样本矩阵
#  长这个样子：
#  1.0 1.1
#  1.0 1.0
#  0.0 0.0
#  0.0 0.1
#######

# 使用k-近邻算法(主要程序算法)
## 参数1: 要训练的点的座标(即要比较的输入值)
## 参数2: 数据集
## 参数3: 所有特征
## 参数4: 比较的次数
def classify0(inX, dataSet, labels, k):
    dataSetSize = dataSet.shape[0]
    diffMat = tile(inX, (dataSetSize, 1)) - dataSet
    sqDiffMat = diffMat ** 2
    sqDistances = sqDiffMat.sum(axis=1)
    distances = sqDistances ** 0.5
    sortedDistIndicies = distances.argsort()
    classCount = {}
    for i in range(k):
        voteIlabel = labels[sortedDistIndicies[i]]
        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
    sortedClassCount = sorted(classCount.items(), 
            key=operator.itemgetter(1), reverse=True)
    return sortedClassCount[0][0]

# 用来将文件转换成矩阵
def file2matrix(filename):
    fr = open(filename)
    arrayOLines = fr.readlines() # 读每一行的数据生成数据数组
    numberOfLines = len(arrayOLines) # 得到行数
    returnMat = zeros((numberOfLines, 3))
    classLabelVector = []
    index = 0
    for line in arrayOLines:
        line = line.strip()
        listFromLine = line.split('\t')
        returnMat[index, :] = listFromLine[0:3]
        classLabelVector.append(int(listFromLine[-1]))
        index += 1
    return returnMat, classLabelVector

## 建立自动标准化的函数
def autoNorm(dataSet):
    minVals = dataSet.min(0) # min(0)返回该矩阵中每一列的最小值, 1就是每一行的
    maxVals = dataSet.max(0)
    ranges = maxVals - minVals
    normDataSet = zeros(shape(dataSet))  # 取一个和dataSet大小一致的零矩阵
    m = dataSet.shape[0]
    normDataSet = dataSet - tile(minVals, (m, 1)) # 将矩阵中的每一个数减去最小值
    normDataSet = normDataSet / tile(ranges, (m, 1)) # 然后除以最大最小差值就可以归一化了
    return normDataSet, ranges, minVals


# 分类器测试代码
def datingClassTest():
    hoRatio = 0.10 # 设置10%的数据为测试集
    datingDateMat, datingLabels = file2matrix('datingTestSet2.txt')
    normMat, ranges, minVals = autoNorm(datingDateMat) # 归一化
    m = normMat.shape[0]
    numTestVecs = int(m * hoRatio) # 总的测试集数目
    errorCount = 0.0
    for i in range(numTestVecs):
        classifierResult = classify0(normMat[i, :], normMat[numTestVecs: m, :], \
                datingLabels[numTestVecs:m], 3)
        print('the classifier came back with: %d, the real answer is: %d'\
                % (classifierResult, datingLabels[i]))
        if (classifierResult != datingLabels[i]):
            errorCount += 1.0
    print('the total error rate is: %f' % (errorCount / float(numTestVecs)))


## 生成预测函数: 通过输入各个参数来分类
def classifyPerson():
    resultList = ['not at all', 'in small doses', 'in large doses'] # 分类结果，有一个可以查
    percentTats = float(input('percentage of time spent playing video games?'))
    ffMiles = float(input('frequent flier miles earned per year?')) 
    iceCream = float(input('liters of ice cream consumed per year?'))
    datingDateMat, datingLabels = file2matrix('datingTestSet2.txt')
    normMat, ranges, minVals = autoNorm(datingDateMat)
    inArr = array([ffMiles, percentTats, iceCream])
    classifierResult = classify0((inArr - minVals) / ranges, normMat, datingLabels, 3)
    print('You will probably like this person: ', \
            resultList[classifierResult - 1])

## 将图像转化为向量
def img2vector(filename):
    returnVect = zeros((1, 1024))
    fr = open(filename)
    for i in range(32):
        lineStr = fr.readline()
        for j in range(32):
            returnVect[0, 32 * i + j] = int(lineStr[j])
    return returnVect

## 识别手写数字测试集
def handwritingClassTest():
    hwLabels = []
    trainingFileList = os.listdir('trainingDigits')
    m = len(trainingFileList)
    trainingMat = zeros((m, 1024))
    for i in range(m):
        fileNameStr = trainingFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = int(fileStr.split('_')[0])
        hwLabels.append(classNumStr)
        trainingMat[i, :] = img2vector('trainingDigits/%s' % fileNameStr)
    testFileList = os.listdir('testDigits')
    errorCount = 0.0
    mTest = len(testFileList)
    for i in range(mTest):
        fileNameStr = testFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = int(fileStr.split('_')[0])
        vectorUnderTest = img2vector('testDigits/%s' % fileNameStr)
        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
        print('the classifier came back with: %d, the real answer is: %d' % (classifierResult, classNumStr))
        if (classifierResult != classNumStr):
            errorCount += 1.0
    print('\nthe total number of errors is: %d' % errorCount)
    print('\nthe total error rate is: %f' % (errorCount / float(mTest)))
